Model of all known spatial maps in primary visual cortex

Framework overview

Architecture of a GCAL model with neurons selective for orientation. A Gaussian input pattern is shown on the retina sheet. Blue cones from the LGN sheets to the retina sheet visualize the receptive field of a single LGN unit. Yellow circles at the LGN level represent the lateral connections within a LGN sheet due to gain control. The activity at the LGN level is shown as well. There, one can see that units on the LGN On sheet are active in regions where the center of the receptive field is brighter than the surround of the receptive field. For the LGN Off sheet, units are active in regions where the surround of the receptive field is brighter than the center. Again, blue circles visualize the receptive fields from V1 to the LGN sheets. Two yellow circles on the V1 sheet represent the excitatory lateral connection (smaller circle) and the inhibitory lateral connection (bigger circle). Whiteish areas on the V1 sheet indicate a slight activation of V1.

Abstract

The primary visual cortex of mammalians is the most extensively studied area in the visual system. The first studies discovered that there is a retinotopic mapping from the retina to the primary visual cortex. Retinotopic mapping is where neighboring neurons in the cortex respond to neighboring locations on the retina. In further research various other cortical maps such as orientation maps and color maps were discovered. All the cortical maps are overlaid onto the same set of neurons, and there is evidence that they interact with each other.

There are a variety of models aiming to replicate the properties of neurons in the primary visual cortex. The majority of these focus on a small subset of all known spatial cortical maps. For this thesis, an all maps model based on the Gain Control, Adaptation, Laterally (GCAL) model has been developed. There, it has been suggested that the underlying principles of firing rate neurons, arranged in two dimensional sheets, using Hebbian learning to adapt to either artificial input patterns or natural images, can account for a variety of different maps, as well as their combination. This required substantial work on the software package in use, Topographica, which led to a superior system to define models. It is likely this will be used by almost all users of Topographica in the future.

This thesis is a small step towards the goal of gaining an understanding of why V1 is wired as it is in mammals, and eventually how the whole visual system works. The improvements in Topographica which have been made in this project have resulted in the production of maintainable, modular models, which will hopefully lead to more exciting research in the area of computational neuroscience of vision. The model which has been built will help in gaining insights to the interplay of the various cortical maps in the primary visual cortex.